Springer Texts in Business and Economics

This book is intended for a first year graduate course in econometrics. I tried to strike a balance between a rigorous approach that proves theorems, and a completely empirical approach where no theorems are proved. Some of the strengths of this book lie in presenting some difficult material in a simple, yet rigorous manner. For example, Chapter 12 on pooling time-series of cross-section data is drawn from my area of expertise in econometrics and the intent here is to make this material more accessible to the general readership of econometrics.

This book teaches some of the basic econometric methods and the underlying assumptions behind them. Estimation, hypotheses testing and prediction are three recurrent themes in this book. Some uses of econometric methods include (i) empirical testing of economic the­ory, whether it is the permanent income consumption theory or purchasing power parity, (ii) forecasting, whether it is GNP or unemployment in the U. S. economy or future sales in the com­puter industry. (iii) Estimation of price elasticities of demand, or returns to scale in production. More importantly, econometric methods can be used to simulate the effect of policy changes like a tax increase on gasoline consumption, or a ban on advertising on cigarette consumption.

It is left to the reader to choose among the available econometric/statistical software to use, like EViews, SAS, Stata, TSP, SHAZAM, Microfit, PcGive, LIMDEP, and RATS, to mention a few. The empirical illustrations in the book utilize a variety of these software packages but mostly with Stata and EViews. Of course, these packages have different advantages and disad­vantages. However, for the basic coverage in this book, these differences may be minor and more a matter of what software the reader is familiar or comfortable with. In most cases, I encourage my students to use more than one of these packages and to verify these results using simple programming languages like GAUSS, OX, R and MATLAB.

This book is not meant to be encyclopedic. I did not attempt the coverage of Bayesian econometrics simply because it is not my comparative advantage. The reader should consult Koop (2003) for a more recent treatment of the subject. Nonparametrics and semiparametrics are popular methods in today’s econometrics, yet they are not covered in this book to keep the technical difficulty at a low level. These are a must for a follow-up course in econometrics, see Li and Racine (2007). Also, for a more rigorous treatment of asymptotic theory, see White (1984). Despite these limitations, the topics covered in this book are basic and necessary in the training of every economist. In fact, it is but a ‘stepping stone’, a ‘sample of the good stuff’ the reader will find in this young, energetic and ever evolving field.

I hope you will share my enthusiasm and optimism in the importance of the tools you will learn when you are through reading this book. Hopefully, it will encourage you to consult the suggested readings on this subject that are referenced at the end of each chapter. In his inaugural lecture at the University of Birmingham, entitled “Econometrics: A View from the Toolroom,” Peter C. B. Phillips (1977) concluded:

“the toolroom may lack the glamour of economics as a practical art in government or business, but it is every bit as important. For the tools (econometricians) fashion provide the key to improvements in our quantitative information concerning matters of economic policy.”

As a student of econometrics, I have benefited from reading Johnston (1984), Kmenta (1986), Theil (1971), Klein (1974), Maddala (1977), and Judge, et al. (1985), to mention a few. As a teacher of undergraduate econometrics, I have learned from Kelejian and Oates (1989), Wallace and Silver (1988), Maddala (1992), Kennedy (1992), Wooldridge (2003) and Stock and Watson (2003). As a teacher of graduate econometrics courses, Greene (1993), Judge, et al. (1985), Fomby, Hill and Johnson (1984) and Davidson and MacKinnon (1993) have been my regular companions. The influence of these books will be evident in the pages that follow. Courses requiring matrix algebra as a pre-requisite to econometrics can start with Chapter 7. Chapter 2 has a quick refresher on some of the required background needed from statistics for the proper understanding of the material in this book.

For an advanced undergraduate/masters class not requiring matrix algebra, one can structure a course based on Chapter 1; Section 2.6 on descriptive statistics; Chapters 3-6; Section 11.1 on simultaneous equations; and Chapter 14 on time-series analysis.

The exercises contain theoretical problems that should supplement the understanding of the material in each chapter. Some of these exercises are drawn from the Problems and Solutions series of Econometric Theory (reprinted with permission of Cambridge University Press). In addition, the book has a set of empirical illustrations demonstrating some of the basic results learned in each chapter. Data sets from published articles are provided for the empirical exer­cises. These exercises are solved using several econometric software packages and are available in the Solution Manual. This book is by no means an applied econometrics text, and the reader should consult Berndt’s (1991) textbook for an excellent treatment of this subject. Instructors and students are encouraged to get other data sets from the internet or journals that provide backup data sets to published articles. The Journal of Applied Econometrics and the Jour­nal of Business and Economic Statistics are two such journals. In fact, the Journal of Applied Econometrics has a replication section for which I am serving as an editor. In my econometrics course, I require my students to replicate an empirical paper. Many students find this experience rewarding in terms of giving them hands on application of econometric methods that prepare them for doing their own empirical work.

I would like to thank my teachers Lawrence R. Klein, Roberto S. Mariano and Robert Shiller who introduced me to this field; James M. Griffin who provided some data sets, empirical exercises and helpful comments, and many colleagues who had direct and indirect influence on the contents of this book including G. S. Maddala, Jan Kmenta, Peter Schmidt, Cheng Hsiao, Tom Wansbeek, Walter Kramer, Maxwell King, Peter C. B. Phillips, Alberto Holly, Essie Maasoumi, Aris Spanos, Farshid Vahid, Heather Anderson, Arnold Zellner and Bryan Brown. Also, I would like to thank my students Wei-Wen Xiong, Ming-Jang Weng, Kiseok Nam, Dong Li, Gustavo Sanchez, Long Liu and Liu Tian who read parts of this book and solved several of the exercises. Martina Bihn at Springer for her continuous support and professional editorial help. I have also benefited from my visits to the University of Arizona, University of California San-Diego, Monash University, the University of Zurich, the Institute of Advanced Studies in Vienna, and the University of Dortmund, Germany. A special thanks to my wife Phyllis whose help and support were essential to completing this book.

References

Berndt, E. R. (1991), The Practice of Econometrics: Classic and Contemporary (Addison-Wesley: Read­ing, MA).

Davidson, R. and J. G. MacKinnon (1993), Estimation and Inference In Econometrics (Oxford University Press: Oxford, MA).

Fomby, T. B., R. C. Hill and S. R. Johnson (1984), Advanced Econometric Methods (Springer-Verlag: New York).

Greene, W. H. (1993), Econometric Analysis (Macmillan: New York ).

Johnston, J. (1984), Econometric Methods, 3rd. Ed., (McGraw-Hill: New York).

Judge, G. G., W. E. Griffiths, R. C. Hill, H. Liitkepohl and T. C. Lee (1985), The Theory and Practice of Econometrics 2nd Ed., (John Wiley: New York).

Kelejian, H. and W. Oates (1989), Introduction to Econometrics: Principles and Applications 2nd Ed., (Harper and Row: New York).

Kennedy, P. (1992), A Guide to Econometrics (The MIT Press: Cambridge, MA).

Klein, L. R. (1974), A Textbook of Econometrics (Prentice-Hall: New Jersey).

Kmenta, J. (1986), Elements of Econometrics 2nd Ed., (Macmillan: New York).

Koop, G. (2003), Bayesian Econometrics (Wiley: New York).

Li, Q. and J. S. Racine (2007), Nonparametric Econometrics, (Princeton University Press: New Jersey). Maddala, G. S. (1977), Econometrics (McGraw-Hill: New York).

Maddala, G. S. (1992), Introduction to Econometrics (Macmillan: New York).

Phillips, P. C.B. (1977), “Econometrics: A View From the Toolroom,” Inaugural Lecture, University of Birmingham, Birmingham, England.

Stock, J. H. and M. W. Watson (2003), Introduction to Econometrics (Addison-Wesley: New York).

Theil, H. (1971), Principles of Econometrics (John Wiley: New York).

Wallace, T. D. and L. Silver (1988), Econometrics: An Introduction (Addison-Wesley: New York).

White, H. (1984), Asymptotic Theory for Econometrics (Academic Press: Florida).

Wooldridge, J. M. (2003), Introductory Econometrics (South-Western: Ohio).

Data

The data sets used in this text can be downloaded from the Springer website in Germany. The address is: http://www. springer. com/978-3-642-20058-8. Please select the link “Samples & Supplements” from the right-hand column.

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